AI Engineer

• Design, develop, and optimize advanced data pipelines using technologies such as Apache Spark, Kafka, Airflow, and cloud-native tools like AWS Glue, Azure Data Factory, or Google Dataflow to drive business insights and enable automation.
• Collaborate with data scientists, architects, and business stakeholders to transform raw data into actionable intelligence, while architecting and maintaining robust data solutions across data lakes, warehouses, and marts using platforms like Snowflake, Redshift, or BigQuery.
• Develop, deploy, and monitor AI/ML models with frameworks such as TensorFlow, PyTorch, and Scikit-Learn, and operationalize these models via APIs and batch or streaming services.
• Ensure data quality, security, and compliance by applying best practices in data governance, encryption, and access control, leveraging tools such as Apache Atlas, Collibra, or cloud security features.
• Research and prototype cutting-edge AI/ML algorithms, evaluate emerging technologies, and communicate findings through technical documentation, dashboards (e.g., Power BI, Tableau), and presentations.
• Champion best practices in CI/CD, automation, and code review to continuously improve the performance, scalability, and reliability of the organization’s data and AI ecosystem.

Lead Data/AI Engineer

• Own AI architecture across cloud platforms (AWS Bedrock, Google Vertex AI)—you'll make the calls on what we build and how
• Design RAG systems end-to-end, from document ingestion and chunking strategy through retrieval optimization and generation quality
• Build agentic systems using modern patterns: MCP (Model Context Protocol), tool-use, function calling, and orchestration frameworks like LangGraph
• Define observability and evaluation so we know our AI is actually working in production—not just demo-ing well
• Manage cost at scale—model selection tradeoffs, token economics, and budget forecasting as we grow
• Establish guardrails and compliance patterns, particularly for healthcare data and HIPAA requirements
• Mentor and guide implementation-level developers as the team grows
• Ship production systems—this isn't a strategy-only role; you'll write code and deploy real solutions

Senior AI Developer – Architect

• Explore and evaluate emerging AI tools, platforms, and agent-based solutions to identify opportunities for the business.
• Design, build, and integrate AI-powered workflows, services, and applications into enterprise systems.
• Help shape and develop internal AI platforms, including agent architectures, permissions, and governance guardrails.
• Guide AI initiatives through structured evaluation and approval processes, ensuring alignment with technical and governance standards.
• Collaborate with teams across Architecture, Security, DevOps, Legal, Privacy, and the wider business to deliver well-rounded AI solutions.
• Translate complex or ambiguous business needs into clear technical requirements, success metrics, and deliverables.
• Monitor performance, adoption, and usage of AI solutions, identifying trends, risks, and opportunities for optimisation.
• Maintain clear documentation, including AI use cases, tool registries, and lifecycle records, ensuring audit-ready standards.
• Support the development of a centralised AI knowledge base to drive consistency and accelerate adoption across teams.
• Contribute to continuous improvement of AI governance, development practices, and delivery frameworks.

AI Engineer, AI Analyst

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• Responsible for architecting solutions achieving customer business outcomes
• Develop and articulate compelling, accurate, and relevant proposals
• Provide technical expertise to sales teams and customers
• Develop customer proposals and critically review them
• Quantify the impact of business problems and position business value
• Communicate how the solution value propositions address customer business needs
• Drive the Account Business Planning process leveraging knowledge of industry trends and customer’s technical environment

AI Architect – Pre-Sales

• Establishes the architectural patterns, evaluation practices, and deployment standards for the team
• Makes framework and model recommendations that set the foundation for how the team builds — evaluates orchestration frameworks, selects deployment patterns, trains and fine-tunes models, and determines where managed platforms end and custom build begins
• Translates proposed business solutions into technical plans — defines product life cycles, prioritizes the backlog, and breaks initiatives into buildable work
• Owns solutions end-to-end: technical planning, architecture, build, deploy, and the monitoring that keeps them honest in production
• Builds the monitoring, evaluation, and regression detection systems that keep production agents reliable — including logging, performance benchmarking, and feedback loops that surface drift early
• Partners with data governance to ensure solutions meet compliance, data quality, and operational standards

Lead AI Engineer, Business Operations

• Evaluate emerging AI tools, platforms, and agents
• Conduct structured technical assessments
• Build and integrate AI workflows and services into enterprise systems
• Collaborate with Architecture, Security, DevOps, Legal, Privacy, and business stakeholders
• Monitor adoption metrics, cost and consumption trends, quality indicators, and post-release performance

AI Engineer / AI Analyst

• Collaborate closely with customers to scope, deploy, and maintain AI solutions in production environments.
• Debug and optimize data pipelines and AI systems running on customer networks.
• Translate complex, often ambiguous customer requirements into well-scoped technical solutions.
• Work across the stack—from model inference on consumer hardware to infrastructure automation.
• Read hardware schematics/logs to identify performance bottlenecks and suggest improvements.
• Serve as a trusted technical advisor to enterprise clients, representing the engineering team externally.
• Contribute feedback and insight to internal teams to continuously improve product robustness and usability.

Forward Deployed AI Engineer

• Design, build, and evolve cloud-native platform services and AI/GenAI capabilities that improve developer productivity and software delivery outcomes.
• Architect and deliver enterprise-grade AI solutions, including RAG and GraphRAG pipelines, multi-agent systems, LLM-powered developer tools, plugins, MCP tools, and reusable skills.
• Drive Claude Suite adoption end-to-end, including claude.ai, Claude API, Claude Code, system prompt design, and context window management.
• Lead large-scale Cursor AI integration, including workspace rules, .cursorrules governance, MCP server connections, and AI-assisted development workflows.
• Build and operate LLM evaluation pipelines using RAGAS, LangSmith, and custom evaluation harnesses.

AVP DevX – AI Engineer, Technical Lead

• Building and deploying LLM-powered, production-grade systems
• End-to-end ownership across model development, optimization, and real-world integration
• Proposing & designing software solutions to address complex business needs
• Writing code, tests, and deploying software
• Preparing technical and procedural documentation required for software
• Maintaining and debugging software

Senior Software Engineer – AI Engineer

• Partner with the AI Process Lead and engineering teams to execute prioritized AI productivity initiatives
• Embed with teams to understand day-to-day coding workflows and identify practical improvement opportunities
• Create and maintain team-level AI artifacts, including AGENTS.md, .github/copilot-instructions.md, reusable prompts, and workflow playbooks
• Build and refine AI personas for concrete use cases (for example: PR reviewer, test gap detector, refactoring assistant)
• Implement AI-assisted code review and developer workflow automations (GitHub, CI/CD, scripts, internal tooling)
• Prototype and deliver tools in Python (or similar), with clear documentation and adoption guidance
• Track and report impact metrics for implemented changes (adoption, cycle time, review quality, defect trends)
• Provide enablement support through demos, office hours, and team training sessions

AI Engineer – Developer Productivity

• Assess how engineering teams are currently using AI tools and identify high-value improvement opportunities.
• Evaluate AI-assisted workflows using measures such as quality, speed, adoption, and cost efficiency.
• Build lightweight instrumentation, analysis, dashboards, or automation tools to measure AI usage patterns and support optimization experiments.
• Partner with engineering leads and individual contributors across multiple teams to drive adoption and behavior change.
• Present findings, proposals, and results to engineering leadership; create clear, compelling documentation and slide decks.
• Stay current on model capabilities, pricing, workflow patterns, and optimization techniques.

AI Engineer – AI Efficiency, Economics

• Lead the end‑to‑end application architecture for IFS Cloud across Projects, FSM, Resource Management, Manufacturing and Finance.
• Define and govern IFS Cloud reference architectures, design patterns, and guardrails, aligned with Evergreen and SaaS operating principles.
• Promote a configuration‑first mindset, minimizing customization and ensuring long‑term maintainability.
• Translate business and operational requirements into scalable, secure, and supportable IFS Cloud solutions.
• Define environment strategies across DEV, TEST, and PROD, supporting secure delivery and operational stability.
• Design extension strategies using standard IFS capabilities (APIs, Business Events, projections, external services).
• Ensure non‑functional requirements, performance, availability, security, and resilience are built into solution designs.
• Architect and enable AI use cases, including embedded IFS AI offerings: Service planning and scheduling optimization, Demand forecasting and anomaly detection
• Design AI‑augmented business processes, promoting AI insights directly into operational workflows.
• Ensure people trust AI recommendations through human-in-the-loop decisioning. Define feedback loops to monitor outcomes and improve AI‑enabled capabilities.
• Architect end-to-end integration solutions between IFS Cloud and upstream CRM platforms, such as lead-to-order, service demand, and contract lifecycle.
• Design event‑triggered integration architectures using IFS Business Events as the primary mechanism for integrations.
• Ensure systems integrate through the common model rather than point‑to‑point mappings, improving scalability and maintainability.
• Promote loose coupling between IFS Cloud and external systems to support Evergreen upgrades and platform evolution.
• Support enterprise reporting, and advanced analytics by ensuring data accuracy and lineage. Collaborate with analytics and data platform teams to align IFS data structures with enterprise BI, AI, and machine learning initiatives.

AI Engineer – Architect, IFS Cloud

• Architect AI Agents that use Tools and Skills including instantiation of MCP clients and servers.
• Architect scalable end-to-end Retrieval-Augmented Generation (RAG) pipelines including data ingestion, document processing, and promoting generation.
• Increase model serving layers and high-throughput data processing pipelines, employing advanced libraries to ensure ultra-low latency and efficient resource use.
• Design and implement secure system architectures for all new AI products ensuring compliance with data governance and security policies.
• Understand how to use Terraform for provisioning and manage Azure infrastructure-as-code.
• Support automated and reproducible Continuous Integration/Continuous Deployment (CI/CD) pipelines.

Senior Agentic AI Architect

• Assess how engineering teams are currently using AI tools and identify high-value improvement opportunities.
• Evaluate AI-assisted workflows and recommend changes that improve outcomes and reduce waste.
• Build lightweight instrumentation, analysis, dashboards, or automation tools to measure AI usage patterns.
• Partner with engineering leads and individual contributors across multiple teams to drive adoption and behavior change.
• Present findings, proposals, and results to engineering leadership; create clear, compelling documentation and slide decks.
• Stay current on model capabilities, pricing, workflow patterns, and optimization techniques.

• Partner with the AI Process Lead and engineering teams to execute prioritized AI productivity initiatives
• Embed with teams to understand day-to-day coding workflows and identify practical improvement opportunities
• Create and maintain team-level AI artifacts, including AGENTS.md , .github/copilot-instructions.md , reusable prompts, and workflow playbooks
• Build and refine AI personas for concrete use cases (for example: PR reviewer, test gap detector, refactoring assistant)
• Implement AI-assisted code review and developer workflow automations (GitHub, CI/CD, scripts, internal tooling)
• Prototype and deliver tools in Python (or similar), with clear documentation and adoption guidance
• Track and report impact metrics for implemented changes (adoption, cycle time, review quality, defect trends)
• Provide enablement support through demos, office hours, and team training sessions

Senior AI Engineer

• Partner with stakeholders across the organization to identify and prioritize high-value opportunities for AI and automation.
• Design, develop, and support AI & automation systems that drive measurable business outcomes—from initial scoping to production deployment.
• Design, build, and maintain scalable infrastructure, including frontend interfaces, backend services, and the underlying AI platform layer.
• Create data pipelines and AI model context protocols, leveraging tools like MCP, LangChain, vector databases, and semantic search.
• Ensure the high availability, performance, and security of AI platform services.
• Contribute to platform growth by authoring SDKs, APIs, and comprehensive developer documentation.
• Translate complex AI concepts for non-technical stakeholders and keep business goals front and center.
• Keep pace with rapidly evolving AI models, frameworks, and research—and bring that knowledge back to the team.

• Define the strategic vision and roadmap for AI architecture practice across the enterprise, identifying high-impact opportunities for AI adoption
• Lead the design and development of robust, scalable, and secure AI architectures, and integration with existing IT infrastructure
• Serve as the top technical leader for AI architecture, guiding the selection of appropriate platforms, frameworks, and cloud technologies
• Build, lead, and mentor a multidisciplinary team of Architects fostering a culture of innovation and best practices
• Collaborate with architects from various functional domains, executive leadership, engineers, security specialists, and business stakeholders to provide direction for AI solutions and articulate complex technical concepts effectively to non-technical audiences
• Stay abreast of emerging AI advancements, such as Generative AI, agentic workflows, and drive innovation through prototyping and validating new technologies.

SVP, Head of AI Architecture

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.

Software Engineer 2 – AI Platform

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

See all jobs on JobTailor

Lead Data/AI Engineer

Senior AI Developer – Architect

AI Engineer, AI Analyst

AI Architect

AI Architect – Pre-Sales

Lead AI Engineer, Business Operations

AI Engineer / AI Analyst

Forward Deployed AI Engineer

AVP DevX – AI Engineer, Technical Lead

Senior Software Engineer – AI Engineer

AI Engineer – Developer Productivity

AI Engineer – AI Efficiency, Economics

AI Engineer – Architect, IFS Cloud

Senior Agentic AI Architect

AI Engineer – AI Efficiency, Economics

Senior AI Engineer

AI Engineer

SVP, Head of AI Architecture

Software Engineer 2 – AI Platform

QuantAI Engineer – Trading, Investment & Optimization